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Flipkart + Monkey Learn Integrations

Syncing Flipkart with Monkey Learn is currently on our roadmap. Leave your email address and we’ll keep you up-to-date with new product releases and inform you when you can start syncing.

About Flipkart

Flipkart is an e-commerce marketplace that offers over 30 million products across 70+ categories. With easy payments and exchanges, free delivery, Flipkart makes shopping a pleasure.

About Monkey Learn

MonkeyLearn is a text analysis platform that helps you identify and extract actionable data from a variety of raw texts, including emails, chats, webpages, papers, tweets, and more! You can use custom tags to categorize texts, such as sentiments or topics, and extract specific data, such as organizations or keywords.

Monkey Learn Integrations
Connect Flipkart + Monkey Learn in easier way

It's easy to connect Flipkart + Monkey Learn without coding knowledge. Start creating your own business flow.

    Triggers
  • New Order

    Triggers when a new order occurred.

  • New Return

    Triggers when a new return occurred.

  • New Shipment

    Triggers when a new shipment occurred.

    Actions
  • Create Product

    Create product listings in Flipkart’s Marketplace.

  • Classify Text

    Classifies texts with a given classifier.

  • Extract Text

    Extracts information from texts with a given extractor.

  • Upload training Data

    Uploads data to a classifier.

How Flipkart & Monkey Learn Integrations Work

  1. Step 1: Choose Flipkart as a trigger app and authenticate it on Appy Pie Connect.

    (30 seconds)

  2. Step 2: Select "Trigger" from the Triggers List.

    (10 seconds)

  3. Step 3: Pick Monkey Learn as an action app and authenticate.

    (30 seconds)

  4. Step 4: Select a resulting action from the Action List.

    (10 seconds)

  5. Step 5: Select the data you want to send from Flipkart to Monkey Learn.

    (2 minutes)

  6. Your Connect is ready! It's time to start enjoying the benefits of workflow automation.

Integration of Flipkart and Monkey Learn

  • Introduction:
  • Hello, my name is Deepak and I am a computer science graduate. My previous experience is in Web Development. I would like to talk about Flipkart and Monkey Learn integration.

    Flipkart is the biggest e-commerce platform in India for products such as smartphones, tablets, laptops and televisions. It is an Indian company which was founded in the year of 2007 by Sachin Bansal and Binny Bansal. It is headquartered in Bangalore. The company has received $4 billion from investors. In 2014, Flipkart had a turnover of $1 billion. In July 2014, Flipkart made a deal with eBay to sell its products. In 2014, Flipkart launched a new app called PhonePe which will allow the customers to transfer money from their bank accounts to any other bank account using their smartphones. In 2015, Flipkart acquired Myntra, a fashion e-commerce company with a value of $330 million. In 2016, Flipkart made a deal with Amazon for selling its products on Flipkart’s website. In 2016, Flipkart acquired Appiterate, a mobile software testing company. In March 2017, Flipkart also started selling groceries across India in a partnership with Bigbasket, a grocery delivery service in India. Flipkart offers cash on delivery for its customers.

    MonkeyLearn?

    MonkeyLearn is a machine learning platform that helps you analyze text data, classify text and extract information from texts even if it’s not stored in a database. It can be integrated easily with websites and apps using webhooks or via an API key. The company offers a free tier for up to 2000 messages per month. MonkeyLearn can be used with Python or PHP programming languages to process texts.

    :

    I would like to talk about how Flipkart can be integrated with MonkeyLearn. First of all, I would like to talk about the benefits of integrating the two services together. The integration of these two services can be made possible through webhooks or API keys.

    Firstly, integration between the two services will help us to transform unstructured data into structured data which can be used for analysis further. Secondly, the integration will help us to automatically categorize the text data into categories or topics thus saving our time and efforts to manually classify it ourselves. Thirdly, integration between the two services can provide us real-time insights through machine learning models thus enabling us to instantly act upon it. Fourthly, the integration between the two services will enable us to generate text analytics reports based on customer’s feedbacks which can help us to improve our service quality. Fifthly, the integration between the two services enables us to automate our workflows. With the use of the integration between the two services we no longer need to manually do tasks that can be done automatically using other things such as other cloud platforms or other applications that help us accomplish the task more quickly than before. Lastly, integration between the two services will enable us to export reports straight into spreadsheets or databases. This allows us to view it anytime or anywhere we want.

    • Integration Between The Two Services Will Help Us To Transform Unstructured Data Into Structured Data Which Can Be Used For Analysis Further:

    Flipkart has over 80 million product pages containing thousands of reviews written by customers every day which contain information that are relevant to our search queries. However, Flipkart does not have any way of classifying these reviews into categories automatically which means that they cannot be analyzed for specific topics directly using search queries because they are all mixed up together in one big list making it impossible for us to find out anything useful from it manually. This information is useful for analysis purposes because this information contains sales history of each product page so if we are able to classify all of them according to their sales history then we can see if there are any changes in sales after certain changes were made on that product page by comparing it with other product pages that have similar sales history which have not been changed recently so that we can identify what could have caused this change in sales and then take actions accordingly based on our findings if it is needed. This analysis gives us much faster results than just analyzing the search queries themselves because there are thousands of search queries but there are only a few hundred of product pages within those searches and only a few hundred reviews within those product pages. We also cannot see what exactly those people searched for when they searched for those product pages which means that we do not know what exactly they wanted from those product pages and we might not be able to give them what they want if we do not know what they want and we might end up losing some of their customers if we do not make any changes on those product pages immediately after we spot problems with them based on our analysis results. However, we still cannot create reports based on those search queries because we cannot see what exactly those people searched for when they searched for those product pages as explained above as well as because we do not know what exactly those people want from those product pages as explained above as well as because we do not know why those people left those product pages without buying anything on those product pages as explained above as well as because we do not know how often those people buy on those product pages as explained above as well as because we cannot compare those search queries with other search queries that we have cplected from other sources such as social media networks or other websites so that we can identify common issues among them and then prioritize them accordingly so that we can start working on fixing problems with those most common issues first instead of wasting our time trying to fix problems with less common issues first.

    • Integration Between The Two Services Will Help Us To Automatically Categorize Text Data Into Categories Or Topics Thus Saving Our Time And Efforts To Manually Classify It Ourselves:

    As discussed above, Flipkart does not have any way of classifying reviews written by their customers automatically into categories or topics related to those reviews so that we can analyze them individually using specific criteria instead of analyzing all of them together using general criteria which means that our analysis results will be inaccurate and incomplete as well as slow and time consuming because as discussed above if we analyze all of them together using general criteria then we will only be able to analyze those reviews together as a whpe which means that we will not be able to analyze those reviews individually according to their content which means that we will not be able to determine whether there are any problems regarding those reviews individually or whether there is a hidden problem within a group of reviews regarding similar topics or whether there is something common among those reviews regarding similar topics so that we can analyze problems individually instead of analyzing them together as a whpe which means that we will have no choice but to pick out the ones that stand out the most (which could be false positives. and then try to analyze whether there are any problems regarding them separately (which will take time. instead of trying to fix problems right away (which will also take time. because if we waste our time analyzing false positives then there might become too many false positives for us (which could be true positives. and then it becomes too difficult for us to find true positives among all false positives (which could be true negatives. so that we might miss some true positives (which could be true negatives. because by then there would probably be too many false positives (which could be true negatives. However, if we analyze all of them together using general criteria then each review will only be analyzed according to its content which means that each review will only be analyzed individually according to its content instead of analyzing them all together as a whpe which means that each review will only be analyzed according to its content instead of analyzing them according to their content cplectively which means that each review will only be analyzed according to its content individually instead of analyzing them according to their content cplectively which means that each review will only be analyzed according to its content respectively which means that each review will only be analyzed according to its content individually instead of analyzing them according to their content cplectively which means that each review will only be analyzed according to its content respectively which means that each review will only be analyzed according to its content individually instead of analyzing them according to their content cplectively which means that each review will only be analyzed according to its content respectively which means that each review will only be analyzed according to its content individually instead of analyzing them according to their content cplectively which means that each review will only be analyzed according to its content respectively which means that each review will only be analyzed according to its content individually instead of analyzing them according to their content cplectively which means that each review will only be analyzed according to its content respectively which means that each review will only be analyzed according to its content individually instead of analyzing them according to their content cplectively which means that

    The process to integrate Flipkart and Monkey Learn may seem complicated and intimidating. This is why Appy Pie Connect has come up with a simple, affordable, and quick spution to help you automate your workflows. Click on the button below to begin.